Abstract

Faces contain a variety of information such as one’s identity and expression. One prevailing model suggests a functional division of labor in processing faces that different aspects of facial information are processed in anatomically separated and functionally encapsulated brain regions. Here, we demonstrate that facial identity and expression can be processed in the same region, yet with different neural coding strategies. To this end, we employed functional magnetic resonance imaging to examine two types of coding schemes, namely univariate activity and multivariate pattern, in the posterior superior temporal cortex (pSTS) - a face-selective region that is traditionally viewed as being specialized for processing facial expression. With the individual difference approach, we found that participants with higher overall face selectivity in the right pSTS were better at differentiating facial expressions measured outside of the scanner. In contrast, individuals whose spatial pattern for faces in the right pSTS was less similar to that for objects were more accurate in identifying previously presented faces. The double dissociation of behavioral relevance between overall neural activity and spatial neural pattern suggests that the functional-division-of-labor model on face processing is over-simplified, and that coding strategies shall be incorporated in a revised model.

Highlights

  • Functional magnetic resonance imaging studies have revealed that brain regions can represent information in at least two ways, namely via univariate activity and multivariate pattern

  • Having identified the posterior superior temporal cortex (pSTS), we investigated the relationship between two neural codes of the pSTS and participants’ behavioral performance in facial identity and expression recognition

  • By comparing the behavioral relevance of univariate activity and multivariate pattern with behavioral performance in facial identity and expression recognition, we found a double dissociation in the right pSTS

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Summary

Introduction

Functional magnetic resonance imaging (fMRI) studies have revealed that brain regions can represent information in at least two ways, namely via univariate activity (i.e., magnitude of neural activity averaged across voxels in a region) and multivariate pattern (i.e., spatial pattern of neural activity among voxels within a region). Multivariate pattern examines the spatial distribution of activity among voxels, and reflects more of a distributed code[15,16]. We hypothesized that facial expression and identity may be both represented in the pSTS yet with localized and distributed codes respectively. We examined the relationship between the coding strategies of the pSTS and behavioral performance in the recognition of facial identity and expression

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